library(tidyverse)

## ── Attaching packages ───────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──

## ✓ ggplot2 3.3.2     ✓ purrr   0.3.4
## ✓ tibble  3.0.3     ✓ dplyr   1.0.2
## ✓ tidyr   1.1.2     ✓ stringr 1.4.0
## ✓ readr   1.3.1     ✓ forcats 0.5.0

## ── Conflicts ──────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(leaflet)
library(ggplot2)
library(plotly)

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

library(readxl)

knitr::opts_chunk$set(
  fig.width = 6,
  fig.asp = .6,
  out.width = "90%"
)
theme_set(theme_minimal() + theme(legend.position = "bottom"))
options(
  ggplot2.continuous.colour = "viridis",
  ggplot2.continuous.fill = "viridis"
)
scale_colour_discrete = scale_color_viridis_d
scale_fill_discrete = scale_fill_viridis_d

Read in wine data.

wine_df = 
  read_csv(
  "./wine_data/tidy/wine_all.csv")

## Warning: Missing column names filled in: 'X1' [1]

## Warning: Duplicated column names deduplicated: 'X1' => 'X1_1' [2]

## Parsed with column specification:
## cols(
##   X1 = col_double(),
##   X1_1 = col_double(),
##   country = col_character(),
##   description = col_character(),
##   designation = col_character(),
##   points = col_double(),
##   price = col_double(),
##   province = col_character(),
##   region_1 = col_character(),
##   taster_name = col_character(),
##   title = col_character(),
##   variety = col_character(),
##   winery = col_character(),
##   year = col_double(),
##   type = col_character()
## )

### remove region 2, taster twitter and missing values in region 1.

#wine_type <- read_csv("./wine_data/winemag-data-130k-v2.csv") %>% 
#            group_by(variety) %>% 
#            count() %>% 
#            arrange(desc(n)) %>% 
#            as.tibble()

Make a plot of distribution of price/rating by type

wine_df %>% 
  filter(!is.na(type),
         price <= 200) %>% 
  ggplot(aes(x = type, y = price, color = type)) +
  geom_violin()

wine_df %>% 
  filter(!is.na(type)) %>% 
  rename(rating = points) %>% 
  ggplot(aes(x = type, y = rating, color = type)) +
  geom_violin()

Make a plot of distribution of price/rating by region

y <- list(
  title = "Mean Price"
)
wine_df %>% 
  filter(!is.na(price)) %>% 
  group_by(country) %>% 
  summarise(mean = mean(price)) %>% 
  mutate(country = fct_reorder(country, mean),
         mean = round(mean, 2),
        text_label=str_c("Country:", country, "\nmean price:", mean)) %>% 
  plot_ly(
  x = ~country, y = ~mean, color = ~country, text = ~text_label, 
  type = "bar", colors = "viridis") %>% 
  layout(yaxis = y)

## `summarise()` ungrouping output (override with `.groups` argument)

## Warning: `arrange_()` is deprecated as of dplyr 0.7.0.
## Please use `arrange()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.

## Warning: Ignoring 1 observations

y <- list(
  title = "Mean rating"
)
wine_df %>% 
  filter(!is.na(points)) %>% 
  group_by(country) %>% 
  summarise(mean = mean(points)) %>% 
  mutate(country = fct_reorder(country, mean),
         mean = round(mean, 2),
        text_label=str_c("Country:", country, "\nmean rating:", mean)) %>% 
  plot_ly(
  x = ~country, y = ~mean, color = ~country, text = ~text_label, 
  type = "bar", colors = "viridis") %>% 
  layout(yaxis = y)

## `summarise()` ungrouping output (override with `.groups` argument)

## Warning: Ignoring 1 observations

Even though Switzerland has the highest priced wine, the average ratings is only ranked at 11.

Make plots of ratings/price by year by wine type.

wine_df %>% 
  filter(!is.na(points),
         !is.na(type),
         year > 1900) %>% 
  group_by(year,type) %>% 
  summarise(mean = mean(points)) %>% 
  mutate(mean = round(mean, 2)) %>% 
  ggplot(aes(x = year, y = mean, color = type))+
  geom_line() +
  labs(y = "Mean rating",
      x = "Year",
      title = "change in mean ratings by wine type and year")

## `summarise()` regrouping output by 'year' (override with `.groups` argument)

wine_df %>% 
  filter(!is.na(price),
         !is.na(type),
         year > 1900) %>% 
  group_by(year,type) %>% 
  summarise(mean = mean(price)) %>% 
  mutate(mean = round(mean, 2)) %>% 
  ggplot(aes(x = year, y = mean, color = type))+
  geom_point()+
  geom_line() +
  labs(y = "Mean price",
      x = "Year",
      title = "change in mean price by wine type in 1900-2017")

## `summarise()` regrouping output by 'year' (override with `.groups` argument)

wine_df %>% 
  filter(!is.na(price),
         !is.na(type),
         year > 2000) %>% 
  group_by(year,type) %>% 
  summarise(mean = mean(price)) %>% 
  mutate(mean = round(mean, 2)) %>% 
  ggplot(aes(x = year, y = mean, color = type))+
  geom_point()+
  geom_line() +
  labs(y = "Mean price",
      x = "Year",
      title = "change in mean price by wine type in 21 century")

## `summarise()` regrouping output by 'year' (override with `.groups` argument)

Make plots of ratings by taster.

y <- list(
  title = "Mean ratings"
)
wine_df %>% 
  filter(!is.na(points)) %>% 
  group_by(taster_name) %>% 
  summarise(mean = mean(points)) %>% 
  mutate(taster_name = fct_reorder(taster_name, mean),
         mean = round(mean, 2),
        text_label=str_c("Taster:", taster_name, "\nmean rating:", mean)) %>% 
  plot_ly(
  x = ~taster_name, y = ~mean, color = ~taster_name, text = ~text_label, 
  type = "bar", colors = "viridis") %>% 
  layout(yaxis = y)

## `summarise()` ungrouping output (override with `.groups` argument)

## Warning: Ignoring 1 observations

who rates the highest price wine? (greater than $800)

wine_df %>% 
  filter(price >= 800,
         !is.na(taster_name)) %>% 
  group_by(taster_name) %>% 
  count()

## # A tibble: 5 x 2
## # Groups:   taster_name [5]
##   taster_name          n
##   <chr>            <int>
## 1 Anne Krebiehl MW     1
## 2 Joe Czerwinski       3
## 3 Kerin O’Keefe        1
## 4 Matt Kettmann        1
## 5 Roger Voss          24

Most of the most expensive wine are rated by Roger Voss.

wine_df %>% 
  filter(!is.na(taster_name)) %>% 
  group_by(taster_name) %>% 
  count() %>% 
  arrange(desc(n)) %>% 
  knitr::kable()

taster_name	n
Roger Voss	25514
Michael Schachner	15134
Kerin O’Keefe	10776
Virginie Boone	9537
Paul Gregutt	9532
Matt Kettmann	6332
Joe Czerwinski	5147
Sean P. Sullivan	4966
Anna Lee C. Iijima	4415
Jim Gordon	4177
Anne Krebiehl MW	3685
Lauren Buzzeo	1835
Susan Kostrzewa	1085
Mike DeSimone	514
Jeff Jenssen	491
Alexander Peartree	415
Carrie Dykes	139
Fiona Adams	27
Christina Pickard	6

Roger Voss rates most number of wine at WineEthusiast Magazine.

Get the winery which has the top average ratings wine.

wine_df %>% 
  filter(!is.na(winery)) %>% 
  group_by(winery,country) %>% 
  summarise(mean = mean(points)) %>% 
  arrange(desc(mean)) %>% 
  top_n(10)

## `summarise()` regrouping output by 'winery' (override with `.groups` argument)

## Selecting by mean

## # A tibble: 16,961 x 3
## # Groups:   winery [16,757]
##    winery                country    mean
##    <chr>                 <chr>     <dbl>
##  1 Araujo                US         98  
##  2 Gandona               US         97  
##  3 J.L. Chave            France     97  
##  4 Ovid                  US         97  
##  5 Standish              Australia  97  
##  6 Salon                 France     96.8
##  7 Tenuta dell'Ornellaia Italy      96.7
##  8 Château Pétrus        France     96.7
##  9 Barons de Rothschild  France     96  
## 10 Bryant Family         US         96  
## # … with 16,951 more rows